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AI evaluation platform
Evaluate AI agents and models against your own criteria offline, then score live production responses (and act on the results without a redeploy).
A good test set is full of real edge cases, but it's still just a snapshot. Models change, user expectations rise, and a prelaunch configuration that passed can slowly stop being good enough.
Build test sets once and apply the same standards across repeated evaluation runs.
Score multiple models, prompts, and parameter sets against the same inputs.
Define what good means for your use case instead of using standard benchmarks.
Run a reusable dataset through combinations of models, instructions, and parameters in offline evaluations. Attach judges to score the outputs and see which variation should ship.
Models don't run in isolation. Agents run with your tools, instructions, and multi-step workflow. Swap a model into an agent config and evaluate it in that context.
Score agent-mode variations the same way you score single-response completions.
Instrument LLM spans to see the decision path beyond the final output.
Group metrics by agent graph to find which agent in the chain caused the regression.
Catch quality drift on the long tail of real user phrasing.
Narrow a regression to the variation that produced it.
Balance evaluation coverage against cost and latency.
Attach judges to config variations to score live responses. Built-in judges cover accuracy, relevance, and toxicity, while custom judges cover your domain. Scores tie back to the responsible variation.
Observability
Evaluations only matter if you can do something about it. Because your prompts, models, and parameters are configuration rather than code, a score can pause a rollout or roll back a variation in production without a deploy.
Detect regressions as exposure ramps, then pause or revert automatically.
Change configs to the last known-good configuration in under 200ms.
Decide the who, what, when, where, and how behind every variation.
The real ‘a-ha’ moment was when we realized we could test models against each other and measure the real user impact. Users are generating better stuff even with the same instructions. As a business, better output directly translates to higher conversion rates, higher retention, and happier customers.
The real ‘a-ha’ moment was when we realized we could test models against each other and measure the real user impact. Users are generating better stuff even with the same instructions. As a business, better output directly translates to higher conversion rates, higher retention, and happier customers.
AI evaluations score how well an AI model or system performs against defined criteria. Offline evaluation scores a candidate prerelease against a fixed dataset. Online evaluation scores live production responses continuously on traffic the test set didn’t contain. Both matter, but they measure different things.
